A Hybrid, Knowledge-Guided Evolutionary Framework for Personalized Compiler Auto-Tuning

• URL: http://arxiv.org/abs/2510.14292v1
• Date: Thu, 16 Oct 2025 04:31:40 GMT
• Title: A Hybrid, Knowledge-Guided Evolutionary Framework for Personalized Compiler Auto-Tuning
• Authors: Haolin Pan, Hongbin Zhang, Mingjie Xing, Yanjun Wu,
• Abstract summary: We propose a novel Hybrid, Knowledge-Guided Evolutionary Framework.<n>This framework intelligently guides online, personalized optimization using knowledge extracted from a large-scale offline analysis phase.<n>In the online stage, a bespoke genetic algorithm leverages this rich knowledge base through specially designed, knowledge-infused genetic operators.
• Score: 11.527479356386706
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Compiler pass auto-tuning is critical for enhancing software performance, yet finding the optimal pass sequence for a specific program is an NP-hard problem. Traditional, general-purpose optimization flags like -O3 and -Oz adopt a one-size-fits-all approach, often failing to unlock a program's full performance potential. To address this challenge, we propose a novel Hybrid, Knowledge-Guided Evolutionary Framework. This framework intelligently guides online, personalized optimization using knowledge extracted from a large-scale offline analysis phase. During the offline stage, we construct a comprehensive compilation knowledge base composed of four key components: (1) Pass Behavioral Vectors to quantitatively capture the effectiveness of each optimization; (2) Pass Groups derived from clustering these vectors based on behavior similarity; (3) a Synergy Pass Graph to model beneficial sequential interactions; and (4) a library of Prototype Pass Sequences evolved for distinct program types. In the online stage, a bespoke genetic algorithm leverages this rich knowledge base through specially designed, knowledge-infused genetic operators. These operators transform the search by performing semantically-aware recombination and targeted, restorative mutations. On a suite of seven public datasets, our framework achieves an average of 11.0% additional LLVM IR instruction reduction over the highly-optimized opt -Oz baseline, demonstrating its state-of-the-art capability in discovering personalized, high-performance optimization sequences.

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